Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations168
Missing cells840
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.0 KiB
Average record size in memory164.8 B

Variable types

Categorical2
Numeric18
Boolean4

Alerts

clorofila_a_ug_l is highly overall correlated with dbo_mg_l and 4 other fieldsHigh correlation
colif_fecales_ufc_100ml is highly overall correlated with icaHigh correlation
color is highly overall correlated with cr_total_mg_l and 1 other fieldsHigh correlation
cr_total_mg_l is highly overall correlated with colorHigh correlation
dbo_mg_l is highly overall correlated with clorofila_a_ug_l and 1 other fieldsHigh correlation
dqo_mg_l is highly overall correlated with microcistina_ug_l and 1 other fieldsHigh correlation
enteroc_ufc_100ml is highly overall correlated with espumasHigh correlation
escher_coli_ufc_100ml is highly overall correlated with clorofila_a_ug_l and 1 other fieldsHigh correlation
espumas is highly overall correlated with enteroc_ufc_100ml and 2 other fieldsHigh correlation
fecha is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
fosf_ortofos_mg_l is highly overall correlated with p_total_l_mg_lHigh correlation
ica is highly overall correlated with clorofila_a_ug_l and 3 other fieldsHigh correlation
mat_susp is highly overall correlated with microcistina_ug_lHigh correlation
microcistina_ug_l is highly overall correlated with dbo_mg_l and 6 other fieldsHigh correlation
nitrato_mg_l is highly overall correlated with clorofila_a_ug_lHigh correlation
od is highly overall correlated with phHigh correlation
olores is highly overall correlated with ica and 1 other fieldsHigh correlation
p_total_l_mg_l is highly overall correlated with fosf_ortofos_mg_l and 1 other fieldsHigh correlation
ph is highly overall correlated with odHigh correlation
sitios is highly overall correlated with color and 2 other fieldsHigh correlation
tem_agua is highly overall correlated with clorofila_a_ug_l and 3 other fieldsHigh correlation
tem_aire is highly overall correlated with fecha and 1 other fieldsHigh correlation
olores is highly imbalanced (62.9%) Imbalance
color is highly imbalanced (60.7%) Imbalance
espumas is highly imbalanced (80.7%) Imbalance
tem_agua has 23 (13.7%) missing values Missing
tem_aire has 24 (14.3%) missing values Missing
od has 36 (21.4%) missing values Missing
ph has 56 (33.3%) missing values Missing
colif_fecales_ufc_100ml has 15 (8.9%) missing values Missing
escher_coli_ufc_100ml has 15 (8.9%) missing values Missing
enteroc_ufc_100ml has 15 (8.9%) missing values Missing
nitrato_mg_l has 17 (10.1%) missing values Missing
nh4_mg_l has 30 (17.9%) missing values Missing
p_total_l_mg_l has 24 (14.3%) missing values Missing
fosf_ortofos_mg_l has 16 (9.5%) missing values Missing
dbo_mg_l has 80 (47.6%) missing values Missing
dqo_mg_l has 72 (42.9%) missing values Missing
turbiedad_ntu has 19 (11.3%) missing values Missing
cr_total_mg_l has 131 (78.0%) missing values Missing
clorofila_a_ug_l has 92 (54.8%) missing values Missing
microcistina_ug_l has 161 (95.8%) missing values Missing
ica has 14 (8.3%) missing values Missing
sitios is uniformly distributed Uniform
clorofila_a_ug_l has 5 (3.0%) zeros Zeros

Reproduction

Analysis started2024-10-30 23:56:42.741457
Analysis finished2024-10-30 23:57:25.836027
Duration43.09 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

sitios
Categorical

High correlation  Uniform 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Canal Villanueva y Río Luján
 
4
Río Lujan y Arroyo Caraguatá
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Other values (37)
148 

Length

Max length41
Median length27.5
Mean length23.238095
Min length8

Characters and Unicode

Total characters3904
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowRío Lujan y Arroyo Caraguatá
3rd rowCanal Aliviador y Río Lujan
4th rowRío Carapachay y Arroyo Gallo Fiambre
5th rowRío Reconquista y Río Lujan

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
2.4%
Río Lujan y Arroyo Caraguatá 4
 
2.4%
Canal Aliviador y Río Lujan 4
 
2.4%
Río Carapachay y Arroyo Gallo Fiambre 4
 
2.4%
Río Reconquista y Río Lujan 4
 
2.4%
Rio Tigre 100m antes del Rio Luján 4
 
2.4%
Río Lujan y Canal San Fernando 4
 
2.4%
Río Capitán y Río San Antonio 4
 
2.4%
Arroyo Abra Vieja y Santa Rosa 4
 
2.4%
Del Arca 4
 
2.4%
Other values (32) 128
76.2%

Length

2024-10-30T20:57:25.945032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 40
 
5.9%
río 36
 
5.3%
de 32
 
4.7%
arroyo 24
 
3.5%
espigón 16
 
2.4%
lujan 16
 
2.4%
canal 12
 
1.8%
la 12
 
1.8%
playa 12
 
1.8%
reserva 12
 
1.8%
Other values (94) 468
68.8%

Most occurring characters

ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

fecha
Categorical

High correlation 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
23/2/2022
42 
4/5/2022
42 
23/8/2022
42 
31/10/2022
35 
no midieron este día
 
4
Other values (2)
 
3

Length

Max length20
Median length15.5
Mean length9.2440476
Min length8

Characters and Unicode

Total characters1553
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row23/2/2022
2nd row23/2/2022
3rd row23/2/2022
4th row23/2/2022
5th row23/2/2022

Common Values

ValueCountFrequency (%)
23/2/2022 42
25.0%
4/5/2022 42
25.0%
23/8/2022 42
25.0%
31/10/2022 35
20.8%
no midieron este día 4
 
2.4%
31/10/0202 2
 
1.2%
no se midió 1
 
0.6%

Length

2024-10-30T20:57:26.086151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-30T20:57:26.222097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
23/2/2022 42
23.1%
4/5/2022 42
23.1%
23/8/2022 42
23.1%
31/10/2022 35
19.2%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
31/10/0202 2
 
1.1%
se 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

tem_agua
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)62.8%
Missing23
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean17.823655
Minimum6
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:26.374608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114.6
median17.9
Q320.4
95-th percentile25.78
Maximum27.4
Range21.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.8486881
Coefficient of variation (CV)0.27203669
Kurtosis-0.40385925
Mean17.823655
Median Absolute Deviation (MAD)3
Skewness-0.073431926
Sum2584.43
Variance23.509776
MonotonicityNot monotonic
2024-10-30T20:57:26.523342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
4.2%
20 6
 
3.6%
18.6 5
 
3.0%
18.5 5
 
3.0%
17 4
 
2.4%
23 3
 
1.8%
17.1 3
 
1.8%
15.6 3
 
1.8%
24.7 3
 
1.8%
18.2 3
 
1.8%
Other values (81) 103
61.3%
(Missing) 23
 
13.7%
ValueCountFrequency (%)
6 1
 
0.6%
7 2
 
1.2%
8 2
 
1.2%
9 1
 
0.6%
10 7
4.2%
10.01 1
 
0.6%
11 1
 
0.6%
11.01 1
 
0.6%
12 1
 
0.6%
12.7 2
 
1.2%
ValueCountFrequency (%)
27.4 1
0.6%
27 1
0.6%
26.5 1
0.6%
26.3 1
0.6%
26.1 2
1.2%
26 1
0.6%
25.8 1
0.6%
25.7 1
0.6%
25.4 2
1.2%
25.2 1
0.6%

tem_aire
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)20.1%
Missing24
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean15.793056
Minimum4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:26.646210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q113
median14
Q319
95-th percentile25.2
Maximum27
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1243509
Coefficient of variation (CV)0.32446862
Kurtosis-0.25796949
Mean15.793056
Median Absolute Deviation (MAD)2
Skewness0.47535054
Sum2274.2
Variance26.258972
MonotonicityNot monotonic
2024-10-30T20:57:26.796380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14 25
14.9%
13 15
 
8.9%
12 12
 
7.1%
16 11
 
6.5%
17 9
 
5.4%
15 7
 
4.2%
27 6
 
3.6%
22 6
 
3.6%
8 5
 
3.0%
23 5
 
3.0%
Other values (19) 43
25.6%
(Missing) 24
14.3%
ValueCountFrequency (%)
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 5
3.0%
9 3
 
1.8%
10 5
3.0%
11 4
 
2.4%
12 12
7.1%
12.3 1
 
0.6%
ValueCountFrequency (%)
27 6
3.6%
26 1
 
0.6%
25.2 3
1.8%
25 1
 
0.6%
23.3 5
3.0%
23 5
3.0%
22.2 2
 
1.2%
22 6
3.6%
21 2
 
1.2%
20 4
2.4%

od
Real number (ℝ)

High correlation  Missing 

Distinct126
Distinct (%)95.5%
Missing36
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean6.7473485
Minimum0.36
Maximum17.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:27.090940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile2.0035
Q15.065
median6.65
Q38.58
95-th percentile10.943
Maximum17.61
Range17.25
Interquartile range (IQR)3.515

Descriptive statistics

Standard deviation2.8357637
Coefficient of variation (CV)0.42027823
Kurtosis0.85228892
Mean6.7473485
Median Absolute Deviation (MAD)1.74
Skewness0.2222464
Sum890.65
Variance8.0415555
MonotonicityNot monotonic
2024-10-30T20:57:27.407689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 2
 
1.2%
4.28 2
 
1.2%
7.85 2
 
1.2%
9 2
 
1.2%
7 2
 
1.2%
5.36 2
 
1.2%
1.5 1
 
0.6%
6.3 1
 
0.6%
4.49 1
 
0.6%
3.85 1
 
0.6%
Other values (116) 116
69.0%
(Missing) 36
 
21.4%
ValueCountFrequency (%)
0.36 1
0.6%
0.45 1
0.6%
1.02 1
0.6%
1.13 1
0.6%
1.39 1
0.6%
1.5 1
0.6%
1.8 1
0.6%
2.17 1
0.6%
2.22 1
0.6%
2.25 1
0.6%
ValueCountFrequency (%)
17.61 1
0.6%
12.84 1
0.6%
12.15 1
0.6%
12 1
0.6%
11.82 1
0.6%
11.05 1
0.6%
11.02 1
0.6%
10.88 1
0.6%
10.83 1
0.6%
10.6 1
0.6%

ph
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)81.2%
Missing56
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean7.57
Minimum5
Maximum10.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:27.734845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.5355
Q17.075
median7.485
Q38.0025
95-th percentile8.884
Maximum10.02
Range5.02
Interquartile range (IQR)0.9275

Descriptive statistics

Standard deviation0.7703632
Coefficient of variation (CV)0.10176528
Kurtosis1.5189961
Mean7.57
Median Absolute Deviation (MAD)0.475
Skewness0.42013799
Sum847.84
Variance0.59345946
MonotonicityNot monotonic
2024-10-30T20:57:28.057567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 6
 
3.6%
7.6 4
 
2.4%
7.76 3
 
1.8%
7.3 3
 
1.8%
7.99 3
 
1.8%
7.5 2
 
1.2%
7.8 2
 
1.2%
7.12 2
 
1.2%
8.11 2
 
1.2%
6.76 2
 
1.2%
Other values (81) 83
49.4%
(Missing) 56
33.3%
ValueCountFrequency (%)
5 1
0.6%
6.2 1
0.6%
6.37 1
0.6%
6.39 1
0.6%
6.48 1
0.6%
6.53 1
0.6%
6.54 1
0.6%
6.56 2
1.2%
6.59 1
0.6%
6.66 1
0.6%
ValueCountFrequency (%)
10.02 1
0.6%
9.98 1
0.6%
9.39 1
0.6%
9.16 1
0.6%
9.01 1
0.6%
8.95 1
0.6%
8.83 1
0.6%
8.81 1
0.6%
8.62 1
0.6%
8.59 1
0.6%

olores
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
156 
True
 
12
ValueCountFrequency (%)
False 156
92.9%
True 12
 
7.1%
2024-10-30T20:57:28.310297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
155 
True
 
13
ValueCountFrequency (%)
False 155
92.3%
True 13
 
7.7%
2024-10-30T20:57:28.666087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
163 
True
 
5
ValueCountFrequency (%)
False 163
97.0%
True 5
 
3.0%
2024-10-30T20:57:28.757314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
False
141 
True
27 
ValueCountFrequency (%)
False 141
83.9%
True 27
 
16.1%
2024-10-30T20:57:28.854936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation  Missing 

Distinct88
Distinct (%)57.5%
Missing15
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean86690.229
Minimum80
Maximum4200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:28.965723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile264
Q11200
median4000
Q340000
95-th percentile274000
Maximum4200000
Range4199920
Interquartile range (IQR)38800

Descriptive statistics

Standard deviation381271.21
Coefficient of variation (CV)4.3980875
Kurtosis91.766626
Mean86690.229
Median Absolute Deviation (MAD)3440
Skewness8.9657969
Sum13263605
Variance1.4536774 × 1011
MonotonicityNot monotonic
2024-10-30T20:57:29.105637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 6
 
3.6%
1000 5
 
3.0%
1800 5
 
3.0%
1400 5
 
3.0%
3000 4
 
2.4%
900 4
 
2.4%
40000 4
 
2.4%
1300 4
 
2.4%
6500 3
 
1.8%
6000 3
 
1.8%
Other values (78) 110
65.5%
(Missing) 15
 
8.9%
ValueCountFrequency (%)
80 1
0.6%
95 1
0.6%
120 1
0.6%
130 1
0.6%
150 1
0.6%
160 1
0.6%
200 1
0.6%
210 1
0.6%
300 2
1.2%
360 1
0.6%
ValueCountFrequency (%)
4200000 1
0.6%
1600000 1
0.6%
1070000 1
0.6%
740000 1
0.6%
700000 1
0.6%
420000 1
0.6%
400000 1
0.6%
280000 1
0.6%
270000 1
0.6%
240000 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

High correlation  Missing 

Distinct77
Distinct (%)50.3%
Missing15
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean4093.3922
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:29.235043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.6
Q1100
median330
Q31700
95-th percentile12960
Maximum150000
Range149999
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation15058.087
Coefficient of variation (CV)3.6786328
Kurtosis62.789246
Mean4093.3922
Median Absolute Deviation (MAD)312
Skewness7.3116318
Sum626289
Variance2.2674597 × 108
MonotonicityNot monotonic
2024-10-30T20:57:29.377940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 17
 
10.1%
200 15
 
8.9%
300 6
 
3.6%
600 5
 
3.0%
500 4
 
2.4%
1000 4
 
2.4%
6 4
 
2.4%
10000 4
 
2.4%
2000 3
 
1.8%
80 3
 
1.8%
Other values (67) 88
52.4%
(Missing) 15
 
8.9%
ValueCountFrequency (%)
1 1
 
0.6%
2 2
1.2%
3 3
1.8%
4 1
 
0.6%
5 1
 
0.6%
6 4
2.4%
9 1
 
0.6%
13 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
150000 1
0.6%
80000 1
0.6%
50000 1
0.6%
44000 1
0.6%
35000 1
0.6%
28000 1
0.6%
15000 1
0.6%
14400 1
0.6%
12000 1
0.6%
11500 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

High correlation  Missing 

Distinct84
Distinct (%)54.9%
Missing15
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean951.70588
Minimum2
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:29.514995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q150
median300
Q3670
95-th percentile2820
Maximum28000
Range27998
Interquartile range (IQR)620

Descriptive statistics

Standard deviation3008.483
Coefficient of variation (CV)3.1611479
Kurtosis54.099694
Mean951.70588
Median Absolute Deviation (MAD)270
Skewness6.9586225
Sum145611
Variance9050970.2
MonotonicityNot monotonic
2024-10-30T20:57:29.648842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
4.2%
10 6
 
3.6%
300 6
 
3.6%
50 6
 
3.6%
1500 5
 
3.0%
2 5
 
3.0%
20 5
 
3.0%
30 4
 
2.4%
40 3
 
1.8%
800 3
 
1.8%
Other values (74) 103
61.3%
(Missing) 15
 
8.9%
ValueCountFrequency (%)
2 5
3.0%
3 1
 
0.6%
4 1
 
0.6%
5 2
 
1.2%
9 1
 
0.6%
10 6
3.6%
11 1
 
0.6%
20 5
3.0%
24 1
 
0.6%
27 1
 
0.6%
ValueCountFrequency (%)
28000 1
0.6%
20000 1
0.6%
12000 1
0.6%
7500 1
0.6%
5000 1
0.6%
4200 1
0.6%
4000 1
0.6%
3300 1
0.6%
2500 1
0.6%
2200 1
0.6%

nitrato_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct84
Distinct (%)55.6%
Missing17
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean6.797351
Minimum1.9
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:29.775784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.1
Q13.7
median5.7
Q38.75
95-th percentile13.6
Maximum21.9
Range20
Interquartile range (IQR)5.05

Descriptive statistics

Standard deviation4.0428034
Coefficient of variation (CV)0.59476161
Kurtosis1.0468284
Mean6.797351
Median Absolute Deviation (MAD)2.4
Skewness1.1188872
Sum1026.4
Variance16.34426
MonotonicityNot monotonic
2024-10-30T20:57:29.925891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 5
 
3.0%
5.1 4
 
2.4%
1.9 4
 
2.4%
5.9 4
 
2.4%
3.7 4
 
2.4%
3.9 4
 
2.4%
5.6 3
 
1.8%
2.9 3
 
1.8%
5.2 3
 
1.8%
2 3
 
1.8%
Other values (74) 114
67.9%
(Missing) 17
 
10.1%
ValueCountFrequency (%)
1.9 4
2.4%
2 3
1.8%
2.1 2
1.2%
2.2 1
 
0.6%
2.4 1
 
0.6%
2.5 1
 
0.6%
2.6 3
1.8%
2.7 3
1.8%
2.8 2
1.2%
2.9 3
1.8%
ValueCountFrequency (%)
21.9 1
0.6%
20.6 1
0.6%
16.4 1
0.6%
16.3 1
0.6%
16.2 1
0.6%
14.8 1
0.6%
14.4 1
0.6%
13.7 1
0.6%
13.5 1
0.6%
13.3 2
1.2%

nh4_mg_l
Real number (ℝ)

Missing 

Distinct84
Distinct (%)60.9%
Missing30
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean2.1604203
Minimum0.049
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:30.064959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.049
5-th percentile0.049
Q10.2225
median0.785
Q31.975
95-th percentile8.62
Maximum23
Range22.951
Interquartile range (IQR)1.7525

Descriptive statistics

Standard deviation4.2998696
Coefficient of variation (CV)1.9902931
Kurtosis14.421316
Mean2.1604203
Median Absolute Deviation (MAD)0.64
Skewness3.7284215
Sum298.138
Variance18.488878
MonotonicityNot monotonic
2024-10-30T20:57:30.198840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.049 12
 
7.1%
0.41 5
 
3.0%
2 5
 
3.0%
0.1 5
 
3.0%
1 4
 
2.4%
1.9 3
 
1.8%
1.3 3
 
1.8%
0.45 3
 
1.8%
0.8 2
 
1.2%
0.75 2
 
1.2%
Other values (74) 94
56.0%
(Missing) 30
 
17.9%
ValueCountFrequency (%)
0.049 12
7.1%
0.05 2
 
1.2%
0.06 2
 
1.2%
0.08 2
 
1.2%
0.1 5
3.0%
0.11 2
 
1.2%
0.12 1
 
0.6%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 1
 
0.6%
ValueCountFrequency (%)
23 2
1.2%
22 2
1.2%
19 1
0.6%
12 1
0.6%
9.3 1
0.6%
8.5 1
0.6%
7.2 1
0.6%
7.1 1
0.6%
5.7 1
0.6%
5.6 1
0.6%

p_total_l_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct71
Distinct (%)49.3%
Missing24
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean0.90305556
Minimum0.1
Maximum30.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:30.327372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.17
Q10.27
median0.385
Q30.575
95-th percentile1.3
Maximum30.12
Range30.02
Interquartile range (IQR)0.305

Descriptive statistics

Standard deviation3.4989713
Coefficient of variation (CV)3.8745914
Kurtosis67.845314
Mean0.90305556
Median Absolute Deviation (MAD)0.145
Skewness8.2552064
Sum130.04
Variance12.2428
MonotonicityNot monotonic
2024-10-30T20:57:30.465852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 8
 
4.8%
0.23 6
 
3.6%
0.24 5
 
3.0%
0.28 4
 
2.4%
0.36 4
 
2.4%
1.2 4
 
2.4%
0.27 4
 
2.4%
0.49 3
 
1.8%
0.29 3
 
1.8%
0.19 3
 
1.8%
Other values (61) 100
59.5%
(Missing) 24
 
14.3%
ValueCountFrequency (%)
0.1 1
 
0.6%
0.11 1
 
0.6%
0.12 1
 
0.6%
0.13 2
1.2%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 2
1.2%
0.18 2
1.2%
0.19 3
1.8%
0.2 1
 
0.6%
ValueCountFrequency (%)
30.12 2
1.2%
2.8 1
 
0.6%
1.9 1
 
0.6%
1.5 1
 
0.6%
1.4 2
1.2%
1.3 2
1.2%
1.2 4
2.4%
1.1 1
 
0.6%
0.95 1
 
0.6%
0.88 1
 
0.6%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct62
Distinct (%)40.8%
Missing16
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean0.41960526
Minimum0.1
Maximum2.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:30.607273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.15
Q10.2375
median0.335
Q30.5
95-th percentile0.928
Maximum2.6
Range2.5
Interquartile range (IQR)0.2625

Descriptive statistics

Standard deviation0.30856618
Coefficient of variation (CV)0.73537252
Kurtosis17.041062
Mean0.41960526
Median Absolute Deviation (MAD)0.115
Skewness3.2691728
Sum63.78
Variance0.095213088
MonotonicityNot monotonic
2024-10-30T20:57:30.744818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 7
 
4.2%
0.2 7
 
4.2%
0.27 7
 
4.2%
0.54 6
 
3.6%
0.31 6
 
3.6%
0.39 5
 
3.0%
0.4 5
 
3.0%
0.18 5
 
3.0%
0.35 4
 
2.4%
0.24 4
 
2.4%
Other values (52) 96
57.1%
(Missing) 16
 
9.5%
ValueCountFrequency (%)
0.1 2
 
1.2%
0.11 2
 
1.2%
0.12 1
 
0.6%
0.13 1
 
0.6%
0.14 1
 
0.6%
0.15 3
1.8%
0.16 2
 
1.2%
0.17 2
 
1.2%
0.18 5
3.0%
0.19 3
1.8%
ValueCountFrequency (%)
2.6 1
0.6%
1.4 2
1.2%
1.3 1
0.6%
1.2 2
1.2%
1 1
0.6%
0.95 1
0.6%
0.91 1
0.6%
0.88 1
0.6%
0.86 2
1.2%
0.85 2
1.2%

dbo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)58.0%
Missing80
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean7.0659091
Minimum1.9
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:30.886187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile1.9
Q13.5
median5.55
Q39.325
95-th percentile15.65
Maximum42
Range40.1
Interquartile range (IQR)5.825

Descriptive statistics

Standard deviation5.6715439
Coefficient of variation (CV)0.80266302
Kurtosis15.813389
Mean7.0659091
Median Absolute Deviation (MAD)2.6
Skewness3.114005
Sum621.8
Variance32.166411
MonotonicityNot monotonic
2024-10-30T20:57:31.043919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 10
 
6.0%
12 5
 
3.0%
5.8 3
 
1.8%
14 3
 
1.8%
11 3
 
1.8%
5.2 3
 
1.8%
5 3
 
1.8%
9.4 3
 
1.8%
6.5 3
 
1.8%
3.5 2
 
1.2%
Other values (41) 50
29.8%
(Missing) 80
47.6%
ValueCountFrequency (%)
1.9 10
6.0%
2 1
 
0.6%
2.3 1
 
0.6%
2.4 2
 
1.2%
2.5 2
 
1.2%
2.6 1
 
0.6%
2.8 1
 
0.6%
3.1 1
 
0.6%
3.3 1
 
0.6%
3.4 1
 
0.6%
ValueCountFrequency (%)
42 1
 
0.6%
21 1
 
0.6%
18 2
 
1.2%
16 1
 
0.6%
15 1
 
0.6%
14 3
1.8%
12 5
3.0%
11 3
1.8%
10 2
 
1.2%
9.4 3
1.8%

dqo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct47
Distinct (%)49.0%
Missing72
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean51.302083
Minimum29
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:31.174581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile29
Q129
median41.5
Q364.25
95-th percentile91
Maximum180
Range151
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation26.944043
Coefficient of variation (CV)0.52520369
Kurtosis5.3700302
Mean51.302083
Median Absolute Deviation (MAD)12.5
Skewness1.9123341
Sum4925
Variance725.98147
MonotonicityNot monotonic
2024-10-30T20:57:31.373357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
29 27
 
16.1%
39 4
 
2.4%
48 3
 
1.8%
33 3
 
1.8%
30 3
 
1.8%
36 3
 
1.8%
46 2
 
1.2%
34 2
 
1.2%
63 2
 
1.2%
54 2
 
1.2%
Other values (37) 45
26.8%
(Missing) 72
42.9%
ValueCountFrequency (%)
29 27
16.1%
30 3
 
1.8%
31 1
 
0.6%
32 2
 
1.2%
33 3
 
1.8%
34 2
 
1.2%
35 1
 
0.6%
36 3
 
1.8%
37 1
 
0.6%
39 4
 
2.4%
ValueCountFrequency (%)
180 1
0.6%
135 1
0.6%
130 1
0.6%
110 1
0.6%
94 1
0.6%
90 1
0.6%
89 1
0.6%
88 1
0.6%
84 1
0.6%
82 2
1.2%

turbiedad_ntu
Real number (ℝ)

Missing 

Distinct56
Distinct (%)37.6%
Missing19
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean35.240268
Minimum2.5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:31.696117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile10.4
Q118
median28
Q345
95-th percentile85
Maximum130
Range127.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation24.106259
Coefficient of variation (CV)0.68405437
Kurtosis1.4221911
Mean35.240268
Median Absolute Deviation (MAD)11
Skewness1.3000315
Sum5250.8
Variance581.11175
MonotonicityNot monotonic
2024-10-30T20:57:32.034113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
4.2%
45 6
 
3.6%
19 6
 
3.6%
22 6
 
3.6%
90 5
 
3.0%
17 5
 
3.0%
28 5
 
3.0%
23 5
 
3.0%
26 5
 
3.0%
13 5
 
3.0%
Other values (46) 94
56.0%
(Missing) 19
 
11.3%
ValueCountFrequency (%)
2.5 1
 
0.6%
3.3 1
 
0.6%
4.1 1
 
0.6%
6 1
 
0.6%
7.5 1
 
0.6%
8.9 1
 
0.6%
9.3 1
 
0.6%
10 1
 
0.6%
11 2
 
1.2%
12 7
4.2%
ValueCountFrequency (%)
130 1
 
0.6%
110 1
 
0.6%
90 5
3.0%
85 2
 
1.2%
84 1
 
0.6%
80 2
 
1.2%
75 3
1.8%
71 1
 
0.6%
70 3
1.8%
67 1
 
0.6%

cr_total_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)62.2%
Missing131
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean2.1137676
Minimum0.005
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:32.316003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.00508
Q10.0064
median0.0082
Q35
95-th percentile8.4
Maximum12
Range11.995
Interquartile range (IQR)4.9936

Descriptive statistics

Standard deviation3.474852
Coefficient of variation (CV)1.643914
Kurtosis0.63241462
Mean2.1137676
Median Absolute Deviation (MAD)0.0022
Skewness1.3536248
Sum78.2094
Variance12.074596
MonotonicityNot monotonic
2024-10-30T20:57:32.595044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.007 5
 
3.0%
6 4
 
2.4%
0.006 3
 
1.8%
0.011 2
 
1.2%
7 2
 
1.2%
0.0061 2
 
1.2%
0.005 2
 
1.2%
5 2
 
1.2%
0.009 1
 
0.6%
0.01 1
 
0.6%
Other values (13) 13
 
7.7%
(Missing) 131
78.0%
ValueCountFrequency (%)
0.005 2
 
1.2%
0.0051 1
 
0.6%
0.006 3
1.8%
0.0061 2
 
1.2%
0.0062 1
 
0.6%
0.0064 1
 
0.6%
0.0069 1
 
0.6%
0.007 5
3.0%
0.0079 1
 
0.6%
0.008 1
 
0.6%
ValueCountFrequency (%)
12 1
 
0.6%
10 1
 
0.6%
8 1
 
0.6%
7 2
1.2%
6 4
2.4%
5 2
1.2%
0.02 1
 
0.6%
0.015 1
 
0.6%
0.011 2
1.2%
0.01 1
 
0.6%

clorofila_a_ug_l
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct67
Distinct (%)88.2%
Missing92
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean524.40263
Minimum0
Maximum6410
Zeros5
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:32.807319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.85
median45.05
Q3451.1
95-th percentile2600
Maximum6410
Range6410
Interquartile range (IQR)448.25

Descriptive statistics

Standard deviation1106.7598
Coefficient of variation (CV)2.1105154
Kurtosis12.687999
Mean524.40263
Median Absolute Deviation (MAD)44.9
Skewness3.3179342
Sum39854.6
Variance1224917.3
MonotonicityNot monotonic
2024-10-30T20:57:32.943974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
3.0%
0.3 3
 
1.8%
350 2
 
1.2%
0.2 2
 
1.2%
0.6 2
 
1.2%
70.8 1
 
0.6%
20.7 1
 
0.6%
130.2 1
 
0.6%
42.1 1
 
0.6%
140.8 1
 
0.6%
Other values (57) 57
33.9%
(Missing) 92
54.8%
ValueCountFrequency (%)
0 5
3.0%
0.1 1
 
0.6%
0.2 2
 
1.2%
0.3 3
1.8%
0.4 1
 
0.6%
0.5 1
 
0.6%
0.6 2
 
1.2%
0.7 1
 
0.6%
0.8 1
 
0.6%
1 1
 
0.6%
ValueCountFrequency (%)
6410 1
0.6%
4650 1
0.6%
3590 1
0.6%
2900 1
0.6%
2500 1
0.6%
2130 1
0.6%
1960 1
0.6%
1730 1
0.6%
1400 1
0.6%
1290 1
0.6%

microcistina_ug_l
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)85.7%
Missing161
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean0.68
Minimum0.19
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:33.046285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.193
Q10.25
median0.4
Q31
95-th percentile1.469
Maximum1.67
Range1.48
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.55949382
Coefficient of variation (CV)0.82278503
Kurtosis-0.069460209
Mean0.68
Median Absolute Deviation (MAD)0.21
Skewness0.97357994
Sum4.76
Variance0.31303333
MonotonicityNot monotonic
2024-10-30T20:57:33.146022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2
 
1.2%
0.2 1
 
0.6%
0.4 1
 
0.6%
0.3 1
 
0.6%
1.67 1
 
0.6%
0.19 1
 
0.6%
(Missing) 161
95.8%
ValueCountFrequency (%)
0.19 1
0.6%
0.2 1
0.6%
0.3 1
0.6%
0.4 1
0.6%
1 2
1.2%
1.67 1
0.6%
ValueCountFrequency (%)
1.67 1
0.6%
1 2
1.2%
0.4 1
0.6%
0.3 1
0.6%
0.2 1
0.6%
0.19 1
0.6%

ica
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)24.7%
Missing14
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean44.071429
Minimum23
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-30T20:57:33.257401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33
Q138
median42
Q350
95-th percentile59.35
Maximum76
Range53
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9487335
Coefficient of variation (CV)0.20305068
Kurtosis0.32568788
Mean44.071429
Median Absolute Deviation (MAD)5
Skewness0.65752044
Sum6787
Variance80.079832
MonotonicityNot monotonic
2024-10-30T20:57:33.374867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
40 13
 
7.7%
42 10
 
6.0%
37 10
 
6.0%
36 9
 
5.4%
38 8
 
4.8%
45 7
 
4.2%
39 7
 
4.2%
41 7
 
4.2%
46 7
 
4.2%
55 6
 
3.6%
Other values (28) 70
41.7%
(Missing) 14
 
8.3%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
29 2
 
1.2%
31 1
 
0.6%
32 2
 
1.2%
33 4
 
2.4%
34 3
 
1.8%
35 5
3.0%
36 9
5.4%
37 10
6.0%
ValueCountFrequency (%)
76 1
 
0.6%
67 1
 
0.6%
64 1
 
0.6%
62 1
 
0.6%
61 2
 
1.2%
60 2
 
1.2%
59 5
3.0%
58 4
2.4%
57 1
 
0.6%
56 2
 
1.2%

Interactions

2024-10-30T20:57:22.546234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.151455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.588581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.536568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.419777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.867254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.670656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.794905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.671820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.790369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.508943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.389149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.074597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.795966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.533921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.216308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.666055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.594859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:22.734651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.313622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.713799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.666965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.623872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.962587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.765977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.882002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.765967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.892529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.596334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.466305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.164658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.884776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.624792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.404861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.764648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.690988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:22.909308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.430964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.847983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.795611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.818597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.057907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.856715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.976901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.864690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.967337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.744923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.560193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.270737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.965726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.715761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.617282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.844883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.794853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.107769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.541028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.049903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.957311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.984849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.164592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.964632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.083003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.964373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.065935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.933075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.734191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.365705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.064858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.823237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.827231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.927144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.874909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.285047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.656708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.231904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.100573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:54.089093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.256895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.061837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.173815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.067605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.148039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.034745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.918000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.454890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.156780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.906195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.041298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.005837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.966280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.464099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:44.756685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.387674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.263885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:54.316850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.349031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.159597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.271008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.184859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.285985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.126456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:09.104789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.549116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.246078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.995994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.216100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.105064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.064667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.664839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:45.015673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.573499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.416716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:54.583427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.454467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.265986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.371849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.302418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.384987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.249026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:09.318388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.646232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.349197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.094846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.433979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.207523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.164007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.794988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:45.141616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.751742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.684986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:54.772280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.565765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.369828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.465090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.419262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.467011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.344836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:09.518812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.744711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.444967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.184125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.649109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.554641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.266208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.896065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:45.258761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:48.956154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.854582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:55.033619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.676997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.484065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.585779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.532983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.580906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.446278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:09.746787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.855964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.545894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.276159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.746243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.674876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.369111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:23.975789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:45.380531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:49.107719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:51.971279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:55.224338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.765981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.581616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.672976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.626129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.660551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.535071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:09.930643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:11.938877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.644164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.364608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.838348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.757392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.474825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.066193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:45.481475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:49.296112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.088141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:55.421927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.862506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.682857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.773790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.754683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.747639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.624984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.094882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.030657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.736155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.454602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:17.924935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.849153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.566221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.145753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:46.420565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:49.436032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.184798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:55.731150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:57.971921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:59.786830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.867148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:03.902618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.829050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.726224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.444687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.114907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.833991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.545947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.006170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:19.957266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.657521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.303990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:46.542923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:49.586716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.295780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:55.947472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.082678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.167715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:01.968550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.047401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:05.920474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.826251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.535764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.213801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:13.926717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.645032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.104688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.044801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.757313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.394773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:46.686910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:49.827125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.447960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.134661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.180313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.276384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.070305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.168278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.016176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:07.925340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.633959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.305951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.025706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.746157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.196280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.133946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.846070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.486011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:46.831656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.010762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.704865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.248167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.282135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.386973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.166090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.320097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.105337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.026752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.734020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.396123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.146102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.838552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.274716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.228467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:21.940931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.583917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.003397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.160537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:52.850977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.380961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.370198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.488916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.302470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.469518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.188263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.117231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.816124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.495990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.238972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:15.926322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.365758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.329727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:22.035688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.664749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.337451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.298346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.003901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.532536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.473877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.599010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.414131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.574945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.296331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.209977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.904799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.586167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.336052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.027570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.469114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.415641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:22.134971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:24.765923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:47.485519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:50.442953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:53.154858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:56.737014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:56:58.587587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:00.697269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:02.570017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:04.669974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:06.417504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:08.304944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:10.994734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:12.684937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:14.446159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:16.114760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:18.569209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:20.514941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-30T20:57:22.346040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-30T20:57:33.490774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
clorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfechafosf_ortofos_mg_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphsitiostem_aguatem_aireturbiedad_ntu
clorofila_a_ug_l1.0000.4830.000-0.4950.5270.261-0.279-0.5150.0000.2440.370-0.5100.0000.3160.1820.5690.2010.0700.2810.1870.000-0.5210.003-0.119
colif_fecales_ufc_100ml0.4831.0000.068-0.4220.3500.2740.2680.2260.2570.0130.307-0.6030.1300.4500.4590.299-0.2930.2930.328-0.1220.039-0.3130.088-0.107
color0.0000.0681.0001.0000.0000.0000.4310.3350.4020.1600.1820.3630.2570.2000.3470.0000.3230.4770.0000.2540.5260.3210.1980.000
cr_total_mg_l-0.495-0.4221.0001.0000.2140.092-0.0510.2440.0690.461-0.191-0.0700.000NaN-0.305-0.1110.1430.0000.311-0.0770.0000.127-0.491-0.060
dbo_mg_l0.5270.3500.0000.2141.0000.196-0.151-0.0590.2010.2050.404-0.4760.0001.0000.4730.1110.2160.2840.4910.2530.134-0.233-0.229-0.254
dqo_mg_l0.2610.2740.0000.0920.1961.0000.0940.2340.0000.3770.045-0.2770.0440.5050.2600.2940.0360.0000.1300.0420.000-0.519-0.4550.249
enteroc_ufc_100ml-0.2790.2680.431-0.051-0.1510.0941.0000.4820.6020.000-0.038-0.1330.2890.450-0.074-0.044-0.2270.3550.032-0.0860.2050.1210.182-0.017
escher_coli_ufc_100ml-0.5150.2260.3350.244-0.0590.2340.4821.0000.4050.000-0.036-0.0680.2040.5450.048-0.119-0.2250.2170.121-0.0740.0830.158-0.0110.029
espumas0.0000.2570.4020.0690.2010.0000.6020.4051.0000.0000.3670.3380.2461.0000.4540.0000.0000.4220.0000.3110.5410.2100.0000.076
fecha0.2440.0130.1600.4610.2050.3770.0000.0000.0001.0000.0690.2340.0000.3870.1580.3060.0890.0000.1330.1060.0000.5830.5990.250
fosf_ortofos_mg_l0.3700.3070.182-0.1910.4040.045-0.038-0.0360.3670.0691.000-0.4870.0000.0540.3230.076-0.2390.3430.8410.2140.000-0.0670.093-0.395
ica-0.510-0.6030.363-0.070-0.476-0.277-0.133-0.0680.3380.234-0.4871.0000.2700.118-0.498-0.2230.2580.560-0.5660.0290.0000.3200.0440.246
mat_susp0.0000.1300.2570.0000.0000.0440.2890.2040.2460.0000.0000.2701.0000.7750.1360.0000.2990.2780.0000.3410.4640.3130.2510.079
microcistina_ug_l0.3160.4500.200NaN1.0000.5050.4500.5451.0000.3870.0540.1180.7751.000-0.0580.072-0.1261.000-0.2520.4061.000-0.0540.091-0.072
nh4_mg_l0.1820.4590.347-0.3050.4730.260-0.0740.0480.4540.1580.323-0.4980.136-0.0581.0000.044-0.3000.4000.323-0.1060.155-0.356-0.288-0.374
nitrato_mg_l0.5690.2990.000-0.1110.1110.294-0.044-0.1190.0000.3060.076-0.2230.0000.0720.0441.0000.1480.0000.0590.1860.000-0.3010.002-0.077
od0.201-0.2930.3230.1430.2160.036-0.227-0.2250.0000.089-0.2390.2580.299-0.126-0.3000.1481.0000.073-0.2590.6050.162-0.279-0.1470.222
olores0.0700.2930.4770.0000.2840.0000.3550.2170.4220.0000.3430.5600.2781.0000.4000.0000.0731.0000.0000.3360.2860.0000.0000.140
p_total_l_mg_l0.2810.3280.0000.3110.4910.1300.0320.1210.0000.1330.841-0.5660.000-0.2520.3230.059-0.2590.0001.0000.1400.000-0.0570.006-0.379
ph0.187-0.1220.254-0.0770.2530.042-0.086-0.0740.3110.1060.2140.0290.3410.406-0.1060.1860.6050.3360.1401.0000.176-0.243-0.1710.025
sitios0.0000.0390.5260.0000.1340.0000.2050.0830.5410.0000.0000.0000.4641.0000.1550.0000.1620.2860.0000.1761.0000.0000.0000.066
tem_agua-0.521-0.3130.3210.127-0.233-0.5190.1210.1580.2100.583-0.0670.3200.313-0.054-0.356-0.301-0.2790.000-0.057-0.2430.0001.0000.713-0.070
tem_aire0.0030.0880.198-0.491-0.229-0.4550.182-0.0110.0000.5990.0930.0440.2510.091-0.2880.002-0.1470.0000.006-0.1710.0000.7131.000-0.154
turbiedad_ntu-0.119-0.1070.000-0.060-0.2540.249-0.0170.0290.0760.250-0.3950.2460.079-0.072-0.374-0.0770.2220.140-0.3790.0250.066-0.070-0.1541.000

Missing values

2024-10-30T20:57:24.907471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-30T20:57:25.237563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-30T20:57:25.544726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitiosfechatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_lica
0Canal Villanueva y Río Luján23/2/202224.523.35.306.56FalseFalseFalseTrue2200.0100.0130.02.90.4200.230.156.229.090.0NaNNaNNaN55.0
1Río Lujan y Arroyo Caraguatá23/2/202225.423.32.256.56TrueTrueFalseFalse1200.0200.0400.03.30.5100.410.355.829.034.0NaNNaNNaN42.0
2Canal Aliviador y Río Lujan23/2/202224.623.32.946.59FalseTrueFalseFalse1800.0200.0580.06.50.0500.590.541.929.017.0NaNNaN0.245.0
3Río Carapachay y Arroyo Gallo Fiambre23/2/202225.223.32.227.45TrueTrueFalseFalse1400.0100.0300.07.41.0000.380.405.829.023.0NaNNaNNaN46.0
4Río Reconquista y Río Lujan23/2/202224.120.01.026.39FalseTrueFalseTrue1100.0100.0370.08.80.0490.550.542.659.018.0NaNNaNNaN44.0
5Rio Tigre 100m antes del Rio Luján23/2/202224.923.33.506.53FalseFalseFalseTrue3200.0200.0750.04.43.5001.100.913.9130.08.9NaNNaNNaN40.0
6Río Lujan y Canal San Fernando23/2/202224.520.01.506.54FalseTrueFalseTrue18000.01500.0100.05.62.0000.730.603.542.012.0NaNNaN0.435.0
7Río Capitán y Río San Antonio23/2/202224.521.06.306.48FalseTrueFalseFalse1000.0200.01200.03.10.0490.170.165.569.090.0NaNNaNNaN46.0
8Arroyo Abra Vieja y Santa Rosa23/2/202223.421.04.496.76FalseFalseFalseFalse400.0100.0220.01.90.1000.210.191.929.039.0NaNNaNNaN58.0
9Del Arca23/2/202221.523.03.856.66FalseFalseFalseTrue2200.0100.0270.05.40.0490.280.391.929.028.0NaNNaNNaN51.0
sitiosfechatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_lica
158Boca Cerrada (Res.Nat. Punta Lara)31/10/2022NaN10.0NaNNaNFalseFalseFalseFalse150.0100.030.06.20.140.600.38NaN72.039.010.074.2NaN41.0
159Camping Eva Perón31/10/202216.07.011.05NaNFalseFalseFalseFalse210.080.090.05.90.150.360.21NaN33.031.0NaN36.50.1938.0
160Toma de agua Club de Pesca31/10/202217.26.08.38NaNFalseFalseFalseFalse95.035.050.05.70.410.290.23NaN46.026.0NaN29.4NaN41.0
161Arroyo El Gato31/10/020218.04.07.36NaNFalseFalseFalseFalse800.0700.0220.05.42.300.360.23NaNNaN23.0NaN16.7NaN37.0
162Ensenada Prefectura Isla Santiago31/10/020217.15.08.98NaNFalseFalseFalseFalse130.030.045.06.10.400.240.24NaNNaN39.0NaN0.6NaN54.0
163Balneario Palo Blanco31/10/202210.012.0NaNNaNFalseFalseFalseTrue800.0600.0400.06.90.380.240.24NaNNaN23.0NaN2.1NaN43.0
164Diagonal 66 (descarga cloaca)31/10/202210.012.0NaNNaNFalseTrueFalseTrue80000.080000.012000.05.21.2030.120.39NaN31.018.2NaN20.2NaN37.0
165Playa La Bagliardi31/10/202210.012.0NaNNaNFalseFalseFalseTrue1400.01000.0380.04.60.800.450.43NaNNaN40.0NaN0.2NaN49.0
166Balneario Municipal31/10/202210.012.0NaNNaNFalseFalseFalseTrue1800.01500.0500.05.20.550.270.27NaN39.090.05.010.5NaN39.0
167Playa La Balandra31/10/202210.012.0NaNNaNFalseFalseFalseTrue900.0600.0480.05.10.210.480.35NaNNaN70.05.048.0NaN34.0